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Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

Klement and El Emam [19] have consolidated these guidelines and checklists into a single set that we refer to as the Consolidated Reporting of Machine Learning Studies (CREMLS) checklist. CREMLS serves as a reporting checklist for journals publishing research describing the development, evaluation, and application of ML models, including all JMIR Publications journals, which have officially adopted these guidelines. CREMLS was developed by identifying existing relevant reporting guidelines and checklists.

Khaled El Emam, Tiffany I Leung, Bradley Malin, William Klement, Gunther Eysenbach

J Med Internet Res 2024;26:e52508

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Article has an altmetric score of 15

Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation

Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation

Under the assumption that an adversary will only attempt one of them, but without knowing which one, the overall probability of one of these attacks being successful is given by the maximum of both [49]: max(A,B) (1) The match rate for population-to-sample attacks is given by El Emam [49] (using the notation in Table 4): This models an adversary who selects a random individual from the population and matches them with records in the real sample.

Khaled El Emam, Lucy Mosquera, Jason Bass

J Med Internet Res 2020;22(11):e23139

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Article has an altmetric score of 24